A New Efficient Method for Computing High-Quality OPF Solutions Based on TRUST-TECH and FFHE

The optimal power flow (OPF) is an important class of nonlinear programming problems for power system operations and planning. It is typically solved by iterative methods based on the gradient descent (e.g., interior-point methods). These methods are often easily trapped near locally optimal solutions and their convergence regions can have fractal boundaries. Sophisticated methods have been developed which can depart from local optimal solutions but result in high computational costs. Transformation under stability-retraining equilibrium characterization (TRUST-TECH or TT) methods thus have been developed which can find (if there are) more than one locally optimal solutions in a tier-by-tier pattern by employing two nonlinear dynamical systems (QGS and PGS), and then tracking down a solution by ODE (ordinary differential equation) solvers. This study proposes a novel scheme based on FFHE (fast flexible holomorphic embedding) to quickly solve nonlinear dynamical systems in TRUST-TECH and designs a new OPF method called TT-FFHE. Its performance is examined using power systems with 5 to 300 buses and attains 10x or more speedups than classical ODE methods for the same requirement of accuracy, which shows the effectiveness of TT-FFHE.